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Neuro-constrained Bio-inspired Recurrent Neural Network
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| Author(s):
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1
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| Subject:
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Artificial Intelligence and Brain Mapping
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Presenter Author:
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| Name:
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Mo Shakiba
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Author:
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| Name 1:
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Mo Shakiba - Corresponding - Presenter
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| Affiliation 1:
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Dept. of Biophysics, Tarbiat Modares University, Tehran, Iran
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| Name 2:
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Rana Rokni
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| Affiliation 2:
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Dept. of CS, Shahid Beheshti University, Tehran, Iran
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| Name 3:
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Mohammad Mohammadi
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| Affiliation 3:
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Dept. of AI, Rajaee Teacher Training University, Tehran, Iran
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Abstract
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Original Research
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introduction :
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Understanding how neural circuits drive sensory processing and decision-making is a core challenge in neuroscience. While traditional Recurrent Neural Networks (RNNs) excel at modeling temporal dynamics, they fall short in capturing the structured synaptic architecture observed in biological networks. Recent advances, such as spatially embedded RNNs (seRNNs), have integrated spatial constraints to enhance biological relevance. However, there remains a gap in leveraging comprehensive anatomical and functional data to improve both task performance and alignment with neural principles.
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Methods:
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We present a bio-inspired RNN that bridges this gap by integrating detailed anatomical connectivity and functional imaging data from the MICrONS dataset. This dataset provides nanometer-scale reconstructions of neural circuits and functional activity recordings from mouse visual cortex. Using neuronal positions, synaptic connections, functional correlations, and Spike Time Tiling Coefficients (STTC), we constrained our model with biologically grounded weight initialization, communicability calculations, and a regularizer to favor realistic network properties.
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Results:
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Our bio-inspired RNN outperformed baseline models across three decision-making tasks: 1-step inference, Go/No-Go, and perceptual decision-making. The anatomically and functionally constrained model achieved the highest accuracies—89.4\%, 96.9\%, and 86.7\%, respectively—and exhibited superior metrics like modularity and small-worldness, reflecting biologically relevant network structures.
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Conclusion:
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These findings demonstrate that incorporating anatomical and functional constraints not only improves task performance but also fosters biologically meaningful network properties. Future work should extend this framework to visual processing and diverse architectures, paving the way for deeper insights into neural dynamics.
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| Attachment:
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| Keywords:
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Neural Networks, Computer; Brain Mapping; Decision Making
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| Key message:
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